Overview

Brought to you by YData

Dataset statistics

Number of variables47
Number of observations36992
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 MiB
Average record size in memory143.0 B

Variable types

Numeric13
Categorical5
Boolean29

Alerts

activity_segment is highly overall correlated with avg_time_spentHigh correlation
age is highly overall correlated with clusterHigh correlation
avg_time_spent is highly overall correlated with activity_segment and 2 other fieldsHigh correlation
avg_transaction_value is highly overall correlated with journey_stage and 2 other fieldsHigh correlation
churn_risk_score is highly overall correlated with membership_category_No Membership and 1 other fieldsHigh correlation
cluster is highly overall correlated with age and 2 other fieldsHigh correlation
days_since_joining is highly overall correlated with joining_yearHigh correlation
internet_option_Mobile_Data is highly overall correlated with internet_option_Wi-FiHigh correlation
internet_option_Wi-Fi is highly overall correlated with internet_option_Mobile_DataHigh correlation
joined_through_referral_No is highly overall correlated with joined_through_referral_YesHigh correlation
joined_through_referral_Yes is highly overall correlated with joined_through_referral_NoHigh correlation
joining_year is highly overall correlated with days_since_joiningHigh correlation
journey_stage is highly overall correlated with avg_transaction_value and 1 other fieldsHigh correlation
medium_of_operation_Desktop is highly overall correlated with medium_of_operation_SmartphoneHigh correlation
medium_of_operation_Smartphone is highly overall correlated with medium_of_operation_DesktopHigh correlation
membership_category_No Membership is highly overall correlated with churn_risk_scoreHigh correlation
offer_application_preference_Yes is highly overall correlated with used_special_discount_YesHigh correlation
points_in_wallet is highly overall correlated with churn_risk_scoreHigh correlation
preferred_offer_types_Gift Vouchers/Coupons is highly overall correlated with preferred_offer_types_Without OffersHigh correlation
preferred_offer_types_Without Offers is highly overall correlated with preferred_offer_types_Gift Vouchers/CouponsHigh correlation
spend_time_ratio is highly overall correlated with avg_time_spent and 2 other fieldsHigh correlation
used_special_discount_Yes is highly overall correlated with offer_application_preference_YesHigh correlation
value_segment is highly overall correlated with avg_transaction_value and 1 other fieldsHigh correlation
gender_Unknown is highly imbalanced (98.3%) Imbalance
medium_of_operation_Both is highly imbalanced (52.2%) Imbalance
feedback_Products always in Stock is highly imbalanced (77.0%) Imbalance
feedback_Quality Customer Care is highly imbalanced (77.3%) Imbalance
feedback_Reasonable Price is highly imbalanced (76.6%) Imbalance
feedback_User Friendly Website is highly imbalanced (76.9%) Imbalance
value_segment is uniformly distributed Uniform
activity_segment is uniformly distributed Uniform
spend_time_ratio has unique values Unique
last_visit_time_hour has 1512 (4.1%) zeros Zeros
last_visit_time_minutes has 645 (1.7%) zeros Zeros
last_visit_time_seconds has 630 (1.7%) zeros Zeros

Reproduction

Analysis started2025-03-09 22:06:38.157243
Analysis finished2025-03-09 22:07:06.035434
Duration27.88 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

age
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.118161
Minimum10
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2025-03-10T03:37:06.362746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q123
median37
Q351
95-th percentile62
Maximum64
Range54
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.867412
Coefficient of variation (CV)0.4274838
Kurtosis-1.1987327
Mean37.118161
Median Absolute Deviation (MAD)14
Skewness-0.0073193193
Sum1373075
Variance251.77477
MonotonicityNot monotonic
2025-03-10T03:37:06.539802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 720
 
1.9%
42 716
 
1.9%
16 716
 
1.9%
38 714
 
1.9%
30 711
 
1.9%
61 709
 
1.9%
60 704
 
1.9%
57 704
 
1.9%
41 699
 
1.9%
59 696
 
1.9%
Other values (45) 29903
80.8%
ValueCountFrequency (%)
10 670
1.8%
11 654
1.8%
12 661
1.8%
13 654
1.8%
14 670
1.8%
15 649
1.8%
16 716
1.9%
17 683
1.8%
18 629
1.7%
19 660
1.8%
ValueCountFrequency (%)
64 672
1.8%
63 656
1.8%
62 677
1.8%
61 709
1.9%
60 704
1.9%
59 696
1.9%
58 678
1.8%
57 704
1.9%
56 682
1.8%
55 695
1.9%

days_since_last_login
Real number (ℝ)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-41.915576
Minimum-999
Maximum26
Zeros0
Zeros (%)0.0%
Negative1999
Negative (%)5.4%
Memory size289.1 KiB
2025-03-10T03:37:06.657064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q18
median12
Q316
95-th percentile22
Maximum26
Range1025
Interquartile range (IQR)8

Descriptive statistics

Standard deviation228.8199
Coefficient of variation (CV)-5.4590661
Kurtosis13.545985
Mean-41.915576
Median Absolute Deviation (MAD)4
Skewness-3.9413558
Sum-1550541
Variance52358.547
MonotonicityNot monotonic
2025-03-10T03:37:06.754434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
12 2380
 
6.4%
13 2373
 
6.4%
14 2307
 
6.2%
15 2278
 
6.2%
11 2262
 
6.1%
10 2091
 
5.7%
16 2068
 
5.6%
-999 1999
 
5.4%
9 1863
 
5.0%
17 1747
 
4.7%
Other values (17) 15624
42.2%
ValueCountFrequency (%)
-999 1999
5.4%
1 328
 
0.9%
2 613
 
1.7%
3 852
2.3%
4 998
2.7%
5 1234
3.3%
6 1257
3.4%
7 1442
3.9%
8 1571
4.2%
9 1863
5.0%
ValueCountFrequency (%)
26 82
 
0.2%
25 203
 
0.5%
24 471
 
1.3%
23 727
2.0%
22 895
2.4%
21 1015
2.7%
20 1184
3.2%
19 1308
3.5%
18 1444
3.9%
17 1747
4.7%

avg_time_spent
Real number (ℝ)

High correlation 

Distinct25961
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.47233
Minimum-2814.1091
Maximum3235.5785
Zeros0
Zeros (%)0.0%
Negative1719
Negative (%)4.6%
Memory size289.1 KiB
2025-03-10T03:37:06.835789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2814.1091
5-th percentile30.15
Q160.1025
median161.765
Q3356.515
95-th percentile1031.0767
Maximum3235.5785
Range6049.6876
Interquartile range (IQR)296.4125

Descriptive statistics

Standard deviation398.28915
Coefficient of variation (CV)1.6358703
Kurtosis5.0039153
Mean243.47233
Median Absolute Deviation (MAD)122.88
Skewness0.53962402
Sum9006528.6
Variance158634.25
MonotonicityNot monotonic
2025-03-10T03:37:06.934321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.1 21
 
0.1%
34.71 20
 
0.1%
33.68 20
 
0.1%
34.33 19
 
0.1%
33.28 18
 
< 0.1%
33.71 18
 
< 0.1%
31.49 18
 
< 0.1%
30.56 18
 
< 0.1%
32.91 18
 
< 0.1%
33.94 17
 
< 0.1%
Other values (25951) 36805
99.5%
ValueCountFrequency (%)
-2814.10911 1
< 0.1%
-2281.236526 1
< 0.1%
-2096.580681 1
< 0.1%
-2093.121606 1
< 0.1%
-2034.80188 1
< 0.1%
-2012.267374 1
< 0.1%
-1960.479169 1
< 0.1%
-1941.035419 1
< 0.1%
-1918.486339 1
< 0.1%
-1913.405154 1
< 0.1%
ValueCountFrequency (%)
3235.578521 1
< 0.1%
3040.41 1
< 0.1%
2899.66 1
< 0.1%
2861.23 1
< 0.1%
2770.56 1
< 0.1%
2766.75 1
< 0.1%
2747.89134 1
< 0.1%
2732.7 1
< 0.1%
2722.077794 1
< 0.1%
2705.756608 1
< 0.1%

avg_transaction_value
Real number (ℝ)

High correlation 

Distinct36894
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29271.194
Minimum800.46
Maximum99914.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2025-03-10T03:37:07.054930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum800.46
5-th percentile3468.9665
Q114177.54
median27554.485
Q340855.11
95-th percentile67338.889
Maximum99914.05
Range99113.59
Interquartile range (IQR)26677.57

Descriptive statistics

Standard deviation19444.806
Coefficient of variation (CV)0.66429836
Kurtosis1.428287
Mean29271.194
Median Absolute Deviation (MAD)13336.775
Skewness1.0110272
Sum1.0828 × 109
Variance3.7810049 × 108
MonotonicityNot monotonic
2025-03-10T03:37:07.290624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30761.37 2
 
< 0.1%
44663.95 2
 
< 0.1%
8876.96 2
 
< 0.1%
28492.9 2
 
< 0.1%
37388.33 2
 
< 0.1%
48254.83 2
 
< 0.1%
16863.84 2
 
< 0.1%
9581.32 2
 
< 0.1%
46738.29 2
 
< 0.1%
21407.75 2
 
< 0.1%
Other values (36884) 36972
99.9%
ValueCountFrequency (%)
800.46 1
< 0.1%
804.34 1
< 0.1%
806.22 1
< 0.1%
806.71 1
< 0.1%
813.82 1
< 0.1%
815.22 1
< 0.1%
821.83 1
< 0.1%
822.7 1
< 0.1%
823.49 1
< 0.1%
823.68 1
< 0.1%
ValueCountFrequency (%)
99914.05 1
< 0.1%
99861.47 1
< 0.1%
99858.02 1
< 0.1%
99819.19 1
< 0.1%
99810.83 1
< 0.1%
99805.52 1
< 0.1%
99803.53 1
< 0.1%
99795.75 1
< 0.1%
99742.63 1
< 0.1%
99730.17 1
< 0.1%

points_in_wallet
Real number (ℝ)

High correlation 

Distinct23700
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686.8822
Minimum-760.66124
Maximum2069.0698
Zeros0
Zeros (%)0.0%
Negative136
Negative (%)0.4%
Memory size289.1 KiB
2025-03-10T03:37:07.453974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-760.66124
5-th percentile351.82615
Q1624.35
median686.8822
Q3757.0025
95-th percentile1028.8753
Maximum2069.0698
Range2829.731
Interquartile range (IQR)132.6525

Descriptive statistics

Standard deviation184.81168
Coefficient of variation (CV)0.26905877
Kurtosis5.1876557
Mean686.8822
Median Absolute Deviation (MAD)66.652199
Skewness-0.08432911
Sum25409146
Variance34155.358
MonotonicityNot monotonic
2025-03-10T03:37:07.612112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
686.8821987 3443
 
9.3%
705.07 9
 
< 0.1%
780.92 8
 
< 0.1%
710.69 7
 
< 0.1%
760.54 7
 
< 0.1%
771.75 7
 
< 0.1%
767.36 6
 
< 0.1%
708.69 6
 
< 0.1%
711.75 6
 
< 0.1%
703.88 6
 
< 0.1%
Other values (23690) 33487
90.5%
ValueCountFrequency (%)
-760.6612363 1
< 0.1%
-549.3574977 1
< 0.1%
-506.2567158 1
< 0.1%
-483.8564006 1
< 0.1%
-471.577009 1
< 0.1%
-469.0203988 1
< 0.1%
-445.2884572 1
< 0.1%
-424.6705248 1
< 0.1%
-412.4416878 1
< 0.1%
-405.2670355 1
< 0.1%
ValueCountFrequency (%)
2069.069761 1
< 0.1%
1816.933696 1
< 0.1%
1780.720173 1
< 0.1%
1763.351594 1
< 0.1%
1759.002532 1
< 0.1%
1755.455512 1
< 0.1%
1755.094693 1
< 0.1%
1751.304195 1
< 0.1%
1750.611562 1
< 0.1%
1736.332594 1
< 0.1%

churn_risk_score
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4633975
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative1163
Negative (%)3.1%
Memory size289.1 KiB
2025-03-10T03:37:07.701560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4096609
Coefficient of variation (CV)0.40701679
Kurtosis1.299243
Mean3.4633975
Median Absolute Deviation (MAD)1
Skewness-1.1143052
Sum128118
Variance1.9871439
MonotonicityNot monotonic
2025-03-10T03:37:07.752605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 10424
28.2%
4 10185
27.5%
5 9827
26.6%
2 2741
 
7.4%
1 2652
 
7.2%
-1 1163
 
3.1%
ValueCountFrequency (%)
-1 1163
 
3.1%
1 2652
 
7.2%
2 2741
 
7.4%
3 10424
28.2%
4 10185
27.5%
5 9827
26.6%
ValueCountFrequency (%)
5 9827
26.6%
4 10185
27.5%
3 10424
28.2%
2 2741
 
7.4%
1 2652
 
7.2%
-1 1163
 
3.1%

joining_day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.687122
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.6 KiB
2025-03-10T03:37:07.837273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.797726
Coefficient of variation (CV)0.56082475
Kurtosis-1.1982075
Mean15.687122
Median Absolute Deviation (MAD)8
Skewness0.013858918
Sum580298
Variance77.399982
MonotonicityNot monotonic
2025-03-10T03:37:07.931253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 1285
 
3.5%
7 1263
 
3.4%
8 1259
 
3.4%
24 1247
 
3.4%
12 1245
 
3.4%
27 1243
 
3.4%
19 1236
 
3.3%
2 1233
 
3.3%
17 1232
 
3.3%
13 1231
 
3.3%
Other values (21) 24518
66.3%
ValueCountFrequency (%)
1 1185
3.2%
2 1233
3.3%
3 1230
3.3%
4 1189
3.2%
5 1285
3.5%
6 1219
3.3%
7 1263
3.4%
8 1259
3.4%
9 1195
3.2%
10 1149
3.1%
ValueCountFrequency (%)
31 690
1.9%
30 1113
3.0%
29 1125
3.0%
28 1203
3.3%
27 1243
3.4%
26 1182
3.2%
25 1183
3.2%
24 1247
3.4%
23 1227
3.3%
22 1226
3.3%

joining_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5334397
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.6 KiB
2025-03-10T03:37:08.001150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4501303
Coefficient of variation (CV)0.52807257
Kurtosis-1.2037093
Mean6.5334397
Median Absolute Deviation (MAD)3
Skewness-0.01362175
Sum241685
Variance11.903399
MonotonicityNot monotonic
2025-03-10T03:37:08.085463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 3194
8.6%
7 3186
8.6%
1 3158
8.5%
8 3147
8.5%
10 3095
8.4%
4 3079
8.3%
6 3076
8.3%
3 3068
8.3%
5 3061
8.3%
9 3061
8.3%
Other values (2) 5867
15.9%
ValueCountFrequency (%)
1 3158
8.5%
2 2844
7.7%
3 3068
8.3%
4 3079
8.3%
5 3061
8.3%
6 3076
8.3%
7 3186
8.6%
8 3147
8.5%
9 3061
8.3%
10 3095
8.4%
ValueCountFrequency (%)
12 3194
8.6%
11 3023
8.2%
10 3095
8.4%
9 3061
8.3%
8 3147
8.5%
7 3186
8.6%
6 3076
8.3%
5 3061
8.3%
4 3079
8.3%
3 3068
8.3%

joining_year
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2017
12540 
2015
12297 
2016
12155 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters147968
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2016
4th row2016
5th row2017

Common Values

ValueCountFrequency (%)
2017 12540
33.9%
2015 12297
33.2%
2016 12155
32.9%

Length

2025-03-10T03:37:08.171498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T03:37:08.256077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2017 12540
33.9%
2015 12297
33.2%
2016 12155
32.9%

Most occurring characters

ValueCountFrequency (%)
2 36992
25.0%
0 36992
25.0%
1 36992
25.0%
7 12540
 
8.5%
5 12297
 
8.3%
6 12155
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147968
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 36992
25.0%
0 36992
25.0%
1 36992
25.0%
7 12540
 
8.5%
5 12297
 
8.3%
6 12155
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common 147968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 36992
25.0%
0 36992
25.0%
1 36992
25.0%
7 12540
 
8.5%
5 12297
 
8.3%
6 12155
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 36992
25.0%
0 36992
25.0%
1 36992
25.0%
7 12540
 
8.5%
5 12297
 
8.3%
6 12155
 
8.2%

last_visit_time_hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.537711
Minimum0
Maximum23
Zeros1512
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2025-03-10T03:37:08.355725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9215733
Coefficient of variation (CV)0.59990872
Kurtosis-1.202568
Mean11.537711
Median Absolute Deviation (MAD)6
Skewness-0.0076141329
Sum426803
Variance47.908177
MonotonicityNot monotonic
2025-03-10T03:37:08.436325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11 1603
 
4.3%
13 1585
 
4.3%
17 1582
 
4.3%
2 1569
 
4.2%
22 1566
 
4.2%
16 1565
 
4.2%
20 1562
 
4.2%
8 1561
 
4.2%
21 1559
 
4.2%
1 1559
 
4.2%
Other values (14) 21281
57.5%
ValueCountFrequency (%)
0 1512
4.1%
1 1559
4.2%
2 1569
4.2%
3 1478
4.0%
4 1547
4.2%
5 1499
4.1%
6 1533
4.1%
7 1516
4.1%
8 1561
4.2%
9 1528
4.1%
ValueCountFrequency (%)
23 1544
4.2%
22 1566
4.2%
21 1559
4.2%
20 1562
4.2%
19 1533
4.1%
18 1504
4.1%
17 1582
4.3%
16 1565
4.2%
15 1547
4.2%
14 1508
4.1%

last_visit_time_minutes
Real number (ℝ)

Zeros 

Distinct60
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.634353
Minimum0
Maximum59
Zeros645
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2025-03-10T03:37:08.537629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q115
median30
Q345
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.300883
Coefficient of variation (CV)0.58381172
Kurtosis-1.1956266
Mean29.634353
Median Absolute Deviation (MAD)15
Skewness-0.008682063
Sum1096234
Variance299.32054
MonotonicityNot monotonic
2025-03-10T03:37:08.669044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 667
 
1.8%
34 667
 
1.8%
41 663
 
1.8%
28 658
 
1.8%
17 657
 
1.8%
50 655
 
1.8%
52 653
 
1.8%
30 651
 
1.8%
58 648
 
1.8%
46 647
 
1.7%
Other values (50) 30426
82.3%
ValueCountFrequency (%)
0 645
1.7%
1 584
1.6%
2 626
1.7%
3 621
1.7%
4 578
1.6%
5 571
1.5%
6 629
1.7%
7 606
1.6%
8 577
1.6%
9 614
1.7%
ValueCountFrequency (%)
59 604
1.6%
58 648
1.8%
57 625
1.7%
56 622
1.7%
55 632
1.7%
54 588
1.6%
53 630
1.7%
52 653
1.8%
51 600
1.6%
50 655
1.8%

last_visit_time_seconds
Real number (ℝ)

Zeros 

Distinct60
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.575205
Minimum0
Maximum59
Zeros630
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2025-03-10T03:37:08.820955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median30
Q345
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.415587
Coefficient of variation (CV)0.58885768
Kurtosis-1.2113213
Mean29.575205
Median Absolute Deviation (MAD)15
Skewness-0.0099451035
Sum1094046
Variance303.30266
MonotonicityNot monotonic
2025-03-10T03:37:08.935662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 673
 
1.8%
32 671
 
1.8%
4 659
 
1.8%
49 654
 
1.8%
50 652
 
1.8%
47 650
 
1.8%
2 649
 
1.8%
51 647
 
1.7%
10 640
 
1.7%
27 639
 
1.7%
Other values (50) 30458
82.3%
ValueCountFrequency (%)
0 630
1.7%
1 625
1.7%
2 649
1.8%
3 624
1.7%
4 659
1.8%
5 622
1.7%
6 576
1.6%
7 621
1.7%
8 629
1.7%
9 634
1.7%
ValueCountFrequency (%)
59 637
1.7%
58 627
1.7%
57 614
1.7%
56 631
1.7%
55 626
1.7%
54 624
1.7%
53 610
1.6%
52 622
1.7%
51 647
1.7%
50 652
1.8%

days_since_joining
Real number (ℝ)

High correlation 

Distinct1096
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3170.7931
Minimum2626
Maximum3721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2025-03-10T03:37:09.052576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2626
5-th percentile2679
Q12895
median3170
Q33448
95-th percentile3667
Maximum3721
Range1095
Interquartile range (IQR)553

Descriptive statistics

Standard deviation317.86093
Coefficient of variation (CV)0.10024651
Kurtosis-1.2123434
Mean3170.7931
Median Absolute Deviation (MAD)277
Skewness0.0096559877
Sum1.1729398 × 108
Variance101035.57
MonotonicityNot monotonic
2025-03-10T03:37:09.170616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3569 55
 
0.1%
3537 51
 
0.1%
3550 50
 
0.1%
3141 49
 
0.1%
3545 49
 
0.1%
2690 48
 
0.1%
2913 48
 
0.1%
2935 48
 
0.1%
3565 47
 
0.1%
3258 47
 
0.1%
Other values (1086) 36500
98.7%
ValueCountFrequency (%)
2626 41
0.1%
2627 31
0.1%
2628 37
0.1%
2629 25
0.1%
2630 33
0.1%
2631 24
0.1%
2632 39
0.1%
2633 43
0.1%
2634 26
0.1%
2635 38
0.1%
ValueCountFrequency (%)
3721 26
0.1%
3720 29
0.1%
3719 37
0.1%
3718 35
0.1%
3717 47
0.1%
3716 35
0.1%
3715 31
0.1%
3714 31
0.1%
3713 33
0.1%
3712 35
0.1%

gender_M
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
18549 
True
18443 
ValueCountFrequency (%)
False 18549
50.1%
True 18443
49.9%
2025-03-10T03:37:09.584577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

gender_Unknown
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
36933 
True
 
59
ValueCountFrequency (%)
False 36933
99.8%
True 59
 
0.2%
2025-03-10T03:37:09.637790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
True
19556 
False
17436 
ValueCountFrequency (%)
True 19556
52.9%
False 17436
47.1%
2025-03-10T03:37:09.671588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
32293 
True
4699 
ValueCountFrequency (%)
False 32293
87.3%
True 4699
 
12.7%
2025-03-10T03:37:09.719552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
30197 
True
6795 
ValueCountFrequency (%)
False 30197
81.6%
True 6795
 
18.4%
2025-03-10T03:37:09.771489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

membership_category_No Membership
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
29300 
True
7692 
ValueCountFrequency (%)
False 29300
79.2%
True 7692
 
20.8%
2025-03-10T03:37:09.815979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
32654 
True
4338 
ValueCountFrequency (%)
False 32654
88.3%
True 4338
 
11.7%
2025-03-10T03:37:09.871715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
32537 
True
4455 
ValueCountFrequency (%)
False 32537
88.0%
True 4455
 
12.0%
2025-03-10T03:37:09.936030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
31004 
True
5988 
ValueCountFrequency (%)
False 31004
83.8%
True 5988
 
16.2%
2025-03-10T03:37:09.988414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

joined_through_referral_No
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
21153 
True
15839 
ValueCountFrequency (%)
False 21153
57.2%
True 15839
42.8%
2025-03-10T03:37:10.028050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

joined_through_referral_Yes
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
21277 
True
15715 
ValueCountFrequency (%)
False 21277
57.5%
True 15715
42.5%
2025-03-10T03:37:10.071240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
24355 
True
12637 
ValueCountFrequency (%)
False 24355
65.8%
True 12637
34.2%
2025-03-10T03:37:10.109722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

preferred_offer_types_Without Offers
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
24911 
True
12081 
ValueCountFrequency (%)
False 24911
67.3%
True 12081
32.7%
2025-03-10T03:37:10.152380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

medium_of_operation_Both
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
33182 
True
3810 
ValueCountFrequency (%)
False 33182
89.7%
True 3810
 
10.3%
2025-03-10T03:37:10.186164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

medium_of_operation_Desktop
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
23079 
True
13913 
ValueCountFrequency (%)
False 23079
62.4%
True 13913
37.6%
2025-03-10T03:37:10.234198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

medium_of_operation_Smartphone
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
23116 
True
13876 
ValueCountFrequency (%)
False 23116
62.5%
True 13876
37.5%
2025-03-10T03:37:10.271972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

internet_option_Mobile_Data
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
24649 
True
12343 
ValueCountFrequency (%)
False 24649
66.6%
True 12343
33.4%
2025-03-10T03:37:10.301722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

internet_option_Wi-Fi
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
24579 
True
12413 
ValueCountFrequency (%)
False 24579
66.4%
True 12413
33.6%
2025-03-10T03:37:10.351992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

used_special_discount_Yes
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
True
20342 
False
16650 
ValueCountFrequency (%)
True 20342
55.0%
False 16650
45.0%
2025-03-10T03:37:10.388318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

offer_application_preference_Yes
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
True
20440 
False
16552 
ValueCountFrequency (%)
True 20440
55.3%
False 16552
44.7%
2025-03-10T03:37:10.436847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
18602 
True
18390 
ValueCountFrequency (%)
False 18602
50.3%
True 18390
49.7%
2025-03-10T03:37:10.472070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
30740 
True
6252 
ValueCountFrequency (%)
False 30740
83.1%
True 6252
 
16.9%
2025-03-10T03:37:10.518408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
30642 
True
6350 
ValueCountFrequency (%)
False 30642
82.8%
True 6350
 
17.2%
2025-03-10T03:37:10.554306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
30721 
True
6271 
ValueCountFrequency (%)
False 30721
83.0%
True 6271
 
17.0%
2025-03-10T03:37:10.601984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
35610 
True
 
1382
ValueCountFrequency (%)
False 35610
96.3%
True 1382
 
3.7%
2025-03-10T03:37:10.645385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
35632 
True
 
1360
ValueCountFrequency (%)
False 35632
96.3%
True 1360
 
3.7%
2025-03-10T03:37:10.688551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

feedback_Reasonable Price
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
35575 
True
 
1417
ValueCountFrequency (%)
False 35575
96.2%
True 1417
 
3.8%
2025-03-10T03:37:10.735584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
30713 
True
6279 
ValueCountFrequency (%)
False 30713
83.0%
True 6279
 
17.0%
2025-03-10T03:37:10.773215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
35601 
True
 
1391
ValueCountFrequency (%)
False 35601
96.2%
True 1391
 
3.8%
2025-03-10T03:37:10.803992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

spend_time_ratio
Real number (ℝ)

High correlation  Unique 

Distinct36992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.34304
Minimum-3067.6899
Maximum12223.076
Zeros0
Zeros (%)0.0%
Negative1719
Negative (%)4.6%
Memory size289.1 KiB
2025-03-10T03:37:10.888858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3067.6899
5-th percentile2.2031875
Q143.331287
median124.62536
Q3328.35823
95-th percentile1080.9273
Maximum12223.076
Range15290.765
Interquartile range (IQR)285.02694

Descriptive statistics

Standard deviation379.97014
Coefficient of variation (CV)1.4212831
Kurtosis35.222401
Mean267.34304
Median Absolute Deviation (MAD)100.23166
Skewness3.2978134
Sum9889553.8
Variance144377.31
MonotonicityNot monotonic
2025-03-10T03:37:11.002147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.8273053 1
 
< 0.1%
18.47416263 1
 
< 0.1%
35.97250366 1
 
< 0.1%
524.8085632 1
 
< 0.1%
-53.31369912 1
 
< 0.1%
511.3103695 1
 
< 0.1%
25.0412492 1
 
< 0.1%
249.4144611 1
 
< 0.1%
187.3775559 1
 
< 0.1%
876.5558231 1
 
< 0.1%
Other values (36982) 36982
> 99.9%
ValueCountFrequency (%)
-3067.689865 1
< 0.1%
-2333.152554 1
< 0.1%
-1966.136724 1
< 0.1%
-1083.952365 1
< 0.1%
-967.0423182 1
< 0.1%
-851.756882 1
< 0.1%
-819.8160472 1
< 0.1%
-725.1484066 1
< 0.1%
-723.0018728 1
< 0.1%
-698.2076 1
< 0.1%
ValueCountFrequency (%)
12223.07553 1
< 0.1%
3161.873515 1
< 0.1%
3126.227997 1
< 0.1%
3114.886829 1
< 0.1%
3052.628526 1
< 0.1%
3000.585298 1
< 0.1%
2994.290887 1
< 0.1%
2994.01187 1
< 0.1%
2990.533912 1
< 0.1%
2968.977364 1
< 0.1%

value_segment
Categorical

High correlation  Uniform 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.4 KiB
Low
12331 
High
12331 
Medium
12330 

Length

Max length6
Median length4
Mean length4.3332883
Min length3

Characters and Unicode

Total characters160297
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowLow
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Low 12331
33.3%
High 12331
33.3%
Medium 12330
33.3%

Length

2025-03-10T03:37:11.121796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T03:37:11.184151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 12331
33.3%
high 12331
33.3%
medium 12330
33.3%

Most occurring characters

ValueCountFrequency (%)
i 24661
15.4%
L 12331
7.7%
w 12331
7.7%
o 12331
7.7%
H 12331
7.7%
g 12331
7.7%
h 12331
7.7%
M 12330
7.7%
e 12330
7.7%
d 12330
7.7%
Other values (2) 24660
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123305
76.9%
Uppercase Letter 36992
 
23.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 24661
20.0%
w 12331
10.0%
o 12331
10.0%
g 12331
10.0%
h 12331
10.0%
e 12330
10.0%
d 12330
10.0%
u 12330
10.0%
m 12330
10.0%
Uppercase Letter
ValueCountFrequency (%)
L 12331
33.3%
H 12331
33.3%
M 12330
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 160297
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 24661
15.4%
L 12331
7.7%
w 12331
7.7%
o 12331
7.7%
H 12331
7.7%
g 12331
7.7%
h 12331
7.7%
M 12330
7.7%
e 12330
7.7%
d 12330
7.7%
Other values (2) 24660
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 24661
15.4%
L 12331
7.7%
w 12331
7.7%
o 12331
7.7%
H 12331
7.7%
g 12331
7.7%
h 12331
7.7%
M 12330
7.7%
e 12330
7.7%
d 12330
7.7%
Other values (2) 24660
15.4%

activity_segment
Categorical

High correlation  Uniform 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.4 KiB
Medium
12332 
Low
12331 
High
12329 

Length

Max length6
Median length4
Mean length4.3333964
Min length3

Characters and Unicode

Total characters160301
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowLow
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 12332
33.3%
Low 12331
33.3%
High 12329
33.3%

Length

2025-03-10T03:37:11.251944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T03:37:11.321930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 12332
33.3%
low 12331
33.3%
high 12329
33.3%

Most occurring characters

ValueCountFrequency (%)
i 24661
15.4%
M 12332
7.7%
e 12332
7.7%
d 12332
7.7%
u 12332
7.7%
m 12332
7.7%
L 12331
7.7%
o 12331
7.7%
w 12331
7.7%
H 12329
7.7%
Other values (2) 24658
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123309
76.9%
Uppercase Letter 36992
 
23.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 24661
20.0%
e 12332
10.0%
d 12332
10.0%
u 12332
10.0%
m 12332
10.0%
o 12331
10.0%
w 12331
10.0%
g 12329
10.0%
h 12329
10.0%
Uppercase Letter
ValueCountFrequency (%)
M 12332
33.3%
L 12331
33.3%
H 12329
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 160301
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 24661
15.4%
M 12332
7.7%
e 12332
7.7%
d 12332
7.7%
u 12332
7.7%
m 12332
7.7%
L 12331
7.7%
o 12331
7.7%
w 12331
7.7%
H 12329
7.7%
Other values (2) 24658
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 24661
15.4%
M 12332
7.7%
e 12332
7.7%
d 12332
7.7%
u 12332
7.7%
m 12332
7.7%
L 12331
7.7%
o 12331
7.7%
w 12331
7.7%
H 12329
7.7%
Other values (2) 24658
15.4%

journey_stage
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Casual User
20947 
High Value Active
9248 
Engaged Regular
6797 

Length

Max length17
Median length11
Mean length13.23497
Min length11

Characters and Unicode

Total characters489588
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh Value Active
2nd rowCasual User
3rd rowEngaged Regular
4th rowCasual User
5th rowCasual User

Common Values

ValueCountFrequency (%)
Casual User 20947
56.6%
High Value Active 9248
25.0%
Engaged Regular 6797
 
18.4%

Length

2025-03-10T03:37:11.400400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T03:37:11.453544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
casual 20947
25.2%
user 20947
25.2%
high 9248
11.1%
value 9248
11.1%
active 9248
11.1%
engaged 6797
 
8.2%
regular 6797
 
8.2%

Most occurring characters

ValueCountFrequency (%)
a 64736
13.2%
e 53037
10.8%
46240
9.4%
s 41894
 
8.6%
l 36992
 
7.6%
u 36992
 
7.6%
g 29639
 
6.1%
r 27744
 
5.7%
C 20947
 
4.3%
U 20947
 
4.3%
Other values (12) 110420
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 360116
73.6%
Uppercase Letter 83232
 
17.0%
Space Separator 46240
 
9.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 64736
18.0%
e 53037
14.7%
s 41894
11.6%
l 36992
10.3%
u 36992
10.3%
g 29639
8.2%
r 27744
7.7%
i 18496
 
5.1%
h 9248
 
2.6%
c 9248
 
2.6%
Other values (4) 32090
8.9%
Uppercase Letter
ValueCountFrequency (%)
C 20947
25.2%
U 20947
25.2%
H 9248
11.1%
V 9248
11.1%
A 9248
11.1%
E 6797
 
8.2%
R 6797
 
8.2%
Space Separator
ValueCountFrequency (%)
46240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 443348
90.6%
Common 46240
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 64736
14.6%
e 53037
12.0%
s 41894
9.4%
l 36992
8.3%
u 36992
8.3%
g 29639
 
6.7%
r 27744
 
6.3%
C 20947
 
4.7%
U 20947
 
4.7%
i 18496
 
4.2%
Other values (11) 91924
20.7%
Common
ValueCountFrequency (%)
46240
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 489588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 64736
13.2%
e 53037
10.8%
46240
9.4%
s 41894
 
8.6%
l 36992
 
7.6%
u 36992
 
7.6%
g 29639
 
6.1%
r 27744
 
5.7%
C 20947
 
4.3%
U 20947
 
4.3%
Other values (12) 110420
22.6%

cluster
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
1
14734 
2
14459 
3
6235 
0
1564 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36992
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 14734
39.8%
2 14459
39.1%
3 6235
16.9%
0 1564
 
4.2%

Length

2025-03-10T03:37:11.552606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T03:37:11.619085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 14734
39.8%
2 14459
39.1%
3 6235
16.9%
0 1564
 
4.2%

Most occurring characters

ValueCountFrequency (%)
1 14734
39.8%
2 14459
39.1%
3 6235
16.9%
0 1564
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36992
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14734
39.8%
2 14459
39.1%
3 6235
16.9%
0 1564
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 36992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14734
39.8%
2 14459
39.1%
3 6235
16.9%
0 1564
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14734
39.8%
2 14459
39.1%
3 6235
16.9%
0 1564
 
4.2%

Interactions

2025-03-10T03:37:03.591795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:47.463111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:48.692952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:49.994100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:51.304629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:52.663307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:54.140700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:55.428366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:56.730239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:58.113289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:59.355051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:00.911885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:02.225177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:03.688068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:47.558298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:48.785104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:50.124510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:51.394123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:52.762092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:54.243155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:55.533715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:56.827280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:58.227579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:59.441015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:01.002909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:02.321163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:03.842360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:47.655133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:48.903665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:50.221310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:51.494344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:52.861924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:54.341374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:55.626201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:56.934348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:58.328157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:59.544279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:01.113230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:02.405519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:03.954834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:47.760342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:49.008439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:50.310311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:51.590625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:52.958689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:54.439781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:55.724787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:57.048090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:58.427504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:59.637680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:01.211912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:02.508022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:04.092344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:47.857385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:49.108234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:50.411856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:51.689809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:53.256934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:54.538674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:55.853893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:57.142725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:58.516742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:59.726915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:01.319942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:02.596743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:04.208633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:47.958620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:49.197132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:50.501566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:51.776295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:53.361014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:54.645633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:55.959665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:57.225967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:58.610822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:59.826314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:01.407256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:02.709049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:04.306938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:48.040313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:49.294160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:50.593104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:51.875437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:53.459484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:54.745018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:56.044361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:57.324598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:58.696128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:59.926764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:01.505487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:02.892957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:04.413493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:48.141199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:49.390163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:50.691757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:51.964645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:53.562992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:54.842139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:56.141579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:57.413245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:58.788236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:00.024620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:01.611601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:02.994107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:04.513365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:48.227363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:49.484610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:50.783772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:52.059530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:53.661474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:54.941697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:56.223143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:57.508849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:58.879079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:00.126497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:01.708226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:03.090564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:04.605376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:48.307106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:49.578094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:50.874869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:52.158160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:53.744389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:55.025549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:56.326324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:57.600407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:58.957898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:00.506020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:01.808981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:03.189167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:04.703971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:48.401356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:49.678252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:50.970399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:52.256566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:53.842588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:55.121592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:56.426766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:57.794180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:59.059704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:00.594887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:01.929152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:03.303170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:04.806155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:48.490151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:49.777639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:51.077688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:52.371826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:53.939214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:55.238088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:56.527661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:57.891403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:59.142955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:00.692276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:02.022305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:03.392903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:04.905118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:48.598183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:49.876403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:51.193656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:52.479012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:54.033434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:55.328833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:56.631271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:57.987210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:36:59.242557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:00.806414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:02.127528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T03:37:03.492150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-10T03:37:11.736501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
activity_segmentageavg_time_spentavg_transaction_valuechurn_risk_scoreclusterdays_since_joiningdays_since_last_loginfeedback_Poor Customer Servicefeedback_Poor Product Qualityfeedback_Poor Websitefeedback_Products always in Stockfeedback_Quality Customer Carefeedback_Reasonable Pricefeedback_Too many adsfeedback_User Friendly Websitegender_Mgender_Unknowninternet_option_Mobile_Datainternet_option_Wi-Fijoined_through_referral_Nojoined_through_referral_Yesjoining_dayjoining_monthjoining_yearjourney_stagelast_visit_time_hourlast_visit_time_minuteslast_visit_time_secondsmedium_of_operation_Bothmedium_of_operation_Desktopmedium_of_operation_Smartphonemembership_category_Gold Membershipmembership_category_No Membershipmembership_category_Platinum Membershipmembership_category_Premium Membershipmembership_category_Silver Membershipoffer_application_preference_Yespast_complaint_Yespoints_in_walletpreferred_offer_types_Gift Vouchers/Couponspreferred_offer_types_Without Offersregion_category_Townregion_category_Villagespend_time_ratioused_special_discount_Yesvalue_segment
activity_segment1.0000.0000.6400.0260.0390.2120.0030.0000.0130.0040.0150.0320.0320.0220.0100.0180.0000.0000.0000.0060.0740.0770.0000.0000.0000.4990.0000.0000.0000.3320.1070.1050.0050.0110.0060.0150.0100.0910.0070.0080.0150.0000.0000.0000.2350.0990.014
age0.0001.0000.003-0.0010.0040.5210.010-0.0030.0080.0000.0080.0000.0070.0030.0160.0080.0000.0000.0000.0200.0000.0000.0000.0050.0100.005-0.014-0.002-0.0070.0150.0000.0000.0000.0070.0000.0000.0130.0000.012-0.0010.0000.0130.0000.005-0.0010.0000.014
avg_time_spent0.6400.0031.0000.019-0.0290.5660.001-0.1000.0110.0000.0110.0350.0350.0210.0020.0250.0000.0000.0130.0010.1130.118-0.0060.0040.0000.455-0.004-0.007-0.0060.3590.1100.1150.0000.0140.0000.0130.0000.1040.0000.0110.0100.0000.0000.014-0.5430.1130.018
avg_transaction_value0.026-0.0010.0191.000-0.2010.3960.003-0.0050.1300.1310.1300.3130.3090.3330.1300.3390.0000.0000.0000.0090.0490.0400.006-0.0030.0000.6930.007-0.0100.0030.0150.0280.0360.0630.1480.1610.1670.0180.0350.0000.1050.0450.0520.0150.0360.5060.0000.880
churn_risk_score0.0390.004-0.029-0.2011.0000.260-0.0110.0160.1870.1890.1870.4630.4580.4700.1880.4620.0000.0000.0000.0090.0640.0590.0060.0080.0040.244-0.0070.002-0.0000.0250.0280.0420.2900.5040.4090.4140.3170.0500.011-0.5420.0700.0830.0170.048-0.0800.0060.197
cluster0.2120.5210.5660.3960.2601.0000.0100.0240.0790.0950.0820.2010.2010.2050.0820.2300.0000.0000.0110.0100.0300.0330.0100.0110.0080.4190.0020.0000.0000.0190.0000.0150.0610.1370.1280.1320.0060.0290.0000.2220.0280.0370.0090.0200.5220.0000.371
days_since_joining0.0030.0100.0010.003-0.0110.0101.0000.0050.0000.0110.0090.0000.0000.0000.0090.0000.0000.0050.0000.0000.0000.005-0.029-0.3360.9310.000-0.002-0.004-0.0090.0000.0000.0120.0130.0140.0160.0170.0000.0150.0090.0040.0060.0000.0000.000-0.0030.0110.000
days_since_last_login0.000-0.003-0.100-0.0050.0160.0240.0051.0000.0100.0040.0040.0000.0000.0000.0000.0080.0030.0050.0000.0000.0090.0080.010-0.0090.0040.0000.005-0.0030.0020.0000.0010.0030.0000.0030.0000.0000.0000.0000.000-0.0010.0050.0050.0000.0000.0780.0000.000
feedback_Poor Customer Service0.0130.0080.0110.1300.1870.0790.0000.0101.0000.2050.2040.0890.0880.0900.2040.0890.0000.0040.0000.0030.0230.0240.0150.0040.0000.0560.0000.0000.0010.0000.0050.0040.0170.0360.0420.0490.0140.0060.0000.0480.0090.0150.0030.0120.0420.0000.047
feedback_Poor Product Quality0.0040.0000.0000.1310.1890.0950.0110.0040.2051.0000.2060.0890.0890.0910.2060.0900.0000.0000.0000.0000.0050.0000.0000.0120.0020.0690.0000.0040.0000.0000.0040.0000.0080.0410.0530.0460.0000.0050.0000.0520.0120.0110.0000.0000.0430.0000.060
feedback_Poor Website0.0150.0080.0110.1300.1870.0820.0090.0040.2040.2061.0000.0890.0880.0900.2040.0890.0000.0000.0000.0070.0190.0150.0100.0130.0000.0620.0000.0090.0140.0100.0000.0150.0140.0410.0440.0430.0030.0030.0060.0430.0070.0040.0070.0080.0390.0000.054
feedback_Products always in Stock0.0320.0000.0350.3130.4630.2010.0000.0000.0890.0890.0891.0000.0380.0390.0890.0380.0110.0000.0000.0000.0320.0340.0060.0120.0000.1560.0110.0060.0000.0180.0100.0210.0360.1010.1130.1170.0170.0310.0000.1030.0310.0420.0080.0240.0970.0000.124
feedback_Quality Customer Care0.0320.0070.0350.3090.4580.2010.0000.0000.0880.0890.0880.0381.0000.0380.0880.0380.0100.0000.0000.0000.0210.0140.0050.0090.0000.1580.0000.0000.0050.0060.0200.0250.0500.1000.1040.1110.0210.0180.0000.1110.0270.0320.0090.0190.0960.0140.129
feedback_Reasonable Price0.0220.0030.0210.3330.4700.2050.0000.0000.0900.0910.0900.0390.0381.0000.0900.0390.0000.0030.0000.0060.0300.0280.0050.0000.0000.1640.0130.0140.0000.0060.0130.0140.0430.1020.1210.1040.0170.0220.0120.1090.0410.0490.0060.0210.1130.0000.132
feedback_Too many ads0.0100.0160.0020.1300.1880.0820.0090.0000.2040.2060.2040.0890.0880.0901.0000.0890.0000.0000.0000.0050.0010.0040.0090.0080.0060.0700.0000.0130.0120.0090.0110.0160.0260.0410.0330.0380.0000.0160.0000.0410.0150.0180.0000.0120.0450.0000.053
feedback_User Friendly Website0.0180.0080.0250.3390.4620.2300.0000.0080.0890.0900.0890.0380.0380.0390.0891.0000.0000.0000.0120.0110.0350.0310.0030.0000.0000.1730.0000.0000.0070.0100.0140.0190.0490.1010.0920.1110.0040.0200.0040.1140.0320.0320.0000.0260.1200.0000.141
gender_M0.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0110.0100.0000.0000.0001.0000.0390.0040.0000.0040.0000.0000.0000.0000.0000.0000.0000.0130.0000.0050.0000.0000.0000.0070.0000.0000.0000.0070.0110.0000.0000.0000.0000.0000.0000.000
gender_Unknown0.0000.0000.0000.0000.0000.0000.0050.0050.0040.0000.0000.0000.0000.0030.0000.0000.0391.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0090.0070.0050.0000.0000.0000.000
internet_option_Mobile_Data0.0000.0000.0130.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0040.0001.0000.5030.0000.0020.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0030.0000.0020.0090.0000.0000.0000.0000.0000.0000.0070.0000.0110.0000.000
internet_option_Wi-Fi0.0060.0200.0010.0090.0090.0100.0000.0000.0030.0000.0070.0000.0000.0060.0050.0110.0000.0000.5031.0000.0000.0050.0000.0030.0000.0000.0000.0000.0000.0000.0020.0040.0070.0000.0000.0120.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.000
joined_through_referral_No0.0740.0000.1130.0490.0640.0300.0000.0090.0230.0050.0190.0320.0210.0300.0010.0350.0040.0000.0000.0001.0000.7440.0000.0000.0000.1150.0070.0070.0070.0570.0260.0000.0080.0220.0150.0180.0050.0210.0000.0150.0000.0000.0000.0000.0200.0170.011
joined_through_referral_Yes0.0770.0000.1180.0400.0590.0330.0050.0080.0240.0000.0150.0340.0140.0280.0040.0310.0000.0000.0020.0050.7441.0000.0000.0120.0030.1160.0060.0000.0070.0580.0220.0050.0050.0230.0160.0120.0000.0140.0000.0100.0000.0000.0000.0000.0160.0200.010
joining_day0.0000.000-0.0060.0060.0060.010-0.0290.0100.0150.0000.0100.0060.0050.0050.0090.0030.0000.0000.0000.0000.0000.0001.0000.0050.0000.000-0.0020.0020.0020.0000.0100.0050.0200.0040.0100.0130.0000.0000.000-0.0020.0090.0080.0000.0160.0060.0080.011
joining_month0.0000.0050.004-0.0030.0080.011-0.336-0.0090.0040.0120.0130.0120.0090.0000.0080.0000.0000.0000.0000.0030.0000.0120.0051.0000.0000.0060.001-0.0070.0020.0110.0210.0080.0000.0000.0000.0090.0000.0060.000-0.0080.0090.0000.0000.0000.0020.0000.000
joining_year0.0000.0100.0000.0000.0040.0080.9310.0040.0000.0020.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0030.0000.0001.0000.0000.0000.0120.0000.0000.0070.0070.0050.0120.0020.0110.0000.0080.0060.0000.0000.0000.0000.0090.0000.0050.000
journey_stage0.4990.0050.4550.6930.2440.4190.0000.0000.0560.0690.0620.1560.1580.1640.0700.1730.0000.0000.0000.0000.1150.1160.0000.0060.0001.0000.0070.0000.0000.2690.0850.0840.0360.0690.0730.0810.0100.0710.0000.0580.0240.0270.0000.0110.1500.0690.577
last_visit_time_hour0.000-0.014-0.0040.007-0.0070.002-0.0020.0050.0000.0000.0000.0110.0000.0130.0000.0000.0000.0000.0000.0000.0070.006-0.0020.0010.0000.0071.000-0.0050.0000.0080.0070.0140.0080.0000.0000.0040.0180.0140.0000.0010.0000.0000.0000.0000.0050.0090.000
last_visit_time_minutes0.000-0.002-0.007-0.0100.0020.000-0.004-0.0030.0000.0040.0090.0060.0000.0140.0130.0000.0000.0150.0150.0000.0070.0000.002-0.0070.0120.000-0.0051.0000.0030.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0100.0060.0000.0070.0000.0010.0100.000
last_visit_time_seconds0.000-0.007-0.0060.003-0.0000.000-0.0090.0020.0010.0000.0140.0000.0050.0000.0120.0070.0130.0000.0000.0000.0070.0070.0020.0020.0000.0000.0000.0031.0000.0060.0000.0030.0000.0160.0070.0000.0000.0000.0000.0040.0000.0000.0110.0000.0110.0000.000
medium_of_operation_Both0.3320.0150.3590.0150.0250.0190.0000.0000.0000.0000.0100.0180.0060.0060.0090.0100.0000.0000.0000.0000.0570.0580.0000.0110.0000.2690.0080.0000.0061.0000.2630.2620.0040.0000.0150.0000.0100.0470.0000.0000.0000.0000.0000.0000.0340.0630.003
medium_of_operation_Desktop0.1070.0000.1100.0280.0280.0000.0000.0010.0050.0040.0000.0100.0200.0130.0110.0140.0050.0000.0000.0020.0260.0220.0100.0210.0070.0850.0070.0000.0000.2631.0000.6010.0010.0050.0010.0000.0000.0200.0000.0100.0000.0040.0030.0000.0260.0120.010
medium_of_operation_Smartphone0.1050.0000.1150.0360.0420.0150.0120.0030.0040.0000.0150.0210.0250.0140.0160.0190.0000.0000.0000.0040.0000.0050.0050.0080.0070.0840.0140.0090.0030.2620.6011.0000.0120.0000.0110.0000.0090.0140.0000.0140.0000.0000.0040.0000.0060.0220.012
membership_category_Gold Membership0.0050.0000.0000.0630.2900.0610.0130.0000.0170.0080.0140.0360.0500.0430.0260.0490.0000.0000.0030.0070.0080.0050.0200.0000.0050.0360.0080.0000.0000.0040.0010.0121.0000.2430.1730.1750.2080.0040.0000.1320.0140.0000.0070.0000.0290.0000.034
membership_category_No Membership0.0110.0070.0140.1480.5040.1370.0140.0030.0360.0410.0410.1010.1000.1020.0410.1010.0000.0000.0000.0000.0220.0230.0040.0000.0120.0690.0000.0000.0160.0000.0050.0000.2431.0000.1870.1890.2250.0060.0000.2700.0120.0120.0000.0080.0490.0050.061
membership_category_Platinum Membership0.0060.0000.0000.1610.4090.1280.0160.0000.0420.0530.0440.1130.1040.1210.0330.0920.0070.0000.0020.0000.0150.0160.0100.0000.0020.0730.0000.0000.0070.0150.0010.0110.1730.1871.0000.1350.1600.0060.0000.2000.0030.0140.0000.0070.0350.0040.063
membership_category_Premium Membership0.0150.0000.0130.1670.4140.1320.0170.0000.0490.0460.0430.1170.1110.1040.0380.1110.0000.0050.0090.0120.0180.0120.0130.0090.0110.0810.0040.0000.0000.0000.0000.0000.1750.1890.1351.0000.1620.0060.0060.2010.0130.0180.0040.0140.0570.0050.066
membership_category_Silver Membership0.0100.0130.0000.0180.3170.0060.0000.0000.0140.0000.0030.0170.0210.0170.0000.0040.0000.0000.0000.0000.0050.0000.0000.0000.0000.0100.0180.0000.0000.0100.0000.0090.2080.2250.1600.1621.0000.0000.0050.1050.0000.0050.0020.0000.0080.0000.007
offer_application_preference_Yes0.0910.0000.1040.0350.0500.0290.0150.0000.0060.0050.0030.0310.0180.0220.0160.0200.0000.0000.0000.0000.0210.0140.0000.0060.0080.0710.0140.0000.0000.0470.0200.0140.0040.0060.0060.0060.0001.0000.0050.0030.0000.0000.0000.0000.0000.8140.012
past_complaint_Yes0.0070.0120.0000.0000.0110.0000.0090.0000.0000.0000.0060.0000.0000.0120.0000.0040.0070.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0050.0051.0000.0000.0000.0030.0000.0000.0030.0050.000
points_in_wallet0.008-0.0010.0110.105-0.5420.2220.004-0.0010.0480.0520.0430.1030.1110.1090.0410.1140.0110.0000.0000.0000.0150.010-0.002-0.0080.0000.0580.0010.0100.0040.0000.0100.0140.1320.2700.2000.2010.1050.0030.0001.0000.0110.0190.0010.0110.0470.0100.049
preferred_offer_types_Gift Vouchers/Coupons0.0150.0000.0100.0450.0700.0280.0060.0050.0090.0120.0070.0310.0270.0410.0150.0320.0000.0090.0000.0040.0000.0000.0090.0090.0000.0240.0000.0060.0000.0000.0000.0000.0140.0120.0030.0130.0000.0000.0000.0111.0000.5020.0040.0050.0200.0000.019
preferred_offer_types_Without Offers0.0000.0130.0000.0520.0830.0370.0000.0050.0150.0110.0040.0420.0320.0490.0180.0320.0000.0070.0000.0000.0000.0000.0080.0000.0000.0270.0000.0000.0000.0000.0040.0000.0000.0120.0140.0180.0050.0000.0030.0190.5021.0000.0000.0070.0220.0000.025
region_category_Town0.0000.0000.0000.0150.0170.0090.0000.0000.0030.0000.0070.0080.0090.0060.0000.0000.0000.0050.0070.0000.0000.0000.0000.0000.0000.0000.0000.0070.0110.0000.0030.0040.0070.0000.0000.0040.0020.0000.0000.0010.0040.0001.0000.4040.0150.0000.000
region_category_Village0.0000.0050.0140.0360.0480.0200.0000.0000.0120.0000.0080.0240.0190.0210.0120.0260.0000.0000.0000.0000.0000.0000.0160.0000.0090.0110.0000.0000.0000.0000.0000.0000.0000.0080.0070.0140.0000.0000.0000.0110.0050.0070.4041.0000.0150.0000.009
spend_time_ratio0.235-0.001-0.5430.506-0.0800.522-0.0030.0780.0420.0430.0390.0970.0960.1130.0450.1200.0000.0000.0110.0000.0200.0160.0060.0020.0000.1500.0050.0010.0110.0340.0260.0060.0290.0490.0350.0570.0080.0000.0030.0470.0200.0220.0150.0151.0000.0200.113
used_special_discount_Yes0.0990.0000.1130.0000.0060.0000.0110.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0170.0200.0080.0000.0050.0690.0090.0100.0000.0630.0120.0220.0000.0050.0040.0050.0000.8140.0050.0100.0000.0000.0000.0000.0201.0000.000
value_segment0.0140.0140.0180.8800.1970.3710.0000.0000.0470.0600.0540.1240.1290.1320.0530.1410.0000.0000.0000.0000.0110.0100.0110.0000.0000.5770.0000.0000.0000.0030.0100.0120.0340.0610.0630.0660.0070.0120.0000.0490.0190.0250.0000.0090.1130.0001.000

Missing values

2025-03-10T03:37:05.153499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-10T03:37:05.619264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agedays_since_last_loginavg_time_spentavg_transaction_valuepoints_in_walletchurn_risk_scorejoining_dayjoining_monthjoining_yearlast_visit_time_hourlast_visit_time_minuteslast_visit_time_secondsdays_since_joininggender_Mgender_Unknownregion_category_Townregion_category_Villagemembership_category_Gold Membershipmembership_category_No Membershipmembership_category_Platinum Membershipmembership_category_Premium Membershipmembership_category_Silver Membershipjoined_through_referral_Nojoined_through_referral_Yespreferred_offer_types_Gift Vouchers/Couponspreferred_offer_types_Without Offersmedium_of_operation_Bothmedium_of_operation_Desktopmedium_of_operation_Smartphoneinternet_option_Mobile_Datainternet_option_Wi-Fiused_special_discount_Yesoffer_application_preference_Yespast_complaint_Yesfeedback_Poor Customer Servicefeedback_Poor Product Qualityfeedback_Poor Websitefeedback_Products always in Stockfeedback_Quality Customer Carefeedback_Reasonable Pricefeedback_Too many adsfeedback_User Friendly Websitespend_time_ratiovalue_segmentactivity_segmentjourney_stagecluster
01817300.6353005.25781.7500002178201716822762FalseFalseFalseTrueFalseFalseTrueFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseTrueTrueTrueFalseFalseFalseFalseTrueFalseFalseFalseFalse175.729370HighHighHigh Value Active3
13216306.3412838.38686.882199128820171238132751FalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalse41.772565LowHighCasual User1
24414516.1621027.00500.6900005111120162253213041FalseFalseTrueFalseFalseTrueFalseFalseFalseFalseTrueTrueFalseFalseTrueFalseFalseTrueFalseTrueTrueFalseFalseTrueFalseFalseFalseFalseFalse40.658597MediumHighEngaged Regular2
3371153.2725239.56567.6600005291020161557503054TrueFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueTrueFalseFalseTrueFalseTrueFalseFalseTrueTrueFalseFalseTrueFalseFalseFalseFalseFalse465.073890MediumLowCasual User1
43120113.1324483.66663.060000512920171546442736FalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueTrueFalseFalseTrueTrueFalseFalseTrueFalseFalseFalseFalseFalse214.524314MediumMediumCasual User1
51323433.6213884.77722.270000381201664673349TrueFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse31.946919LowHighEngaged Regular1
6211055.388982.50756.21000031932015114043644TrueFalseTrueFalseTrueFalseFalseFalseFalseFalseTrueTrueFalseFalseTrueFalseTrueFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse159.320681LowLowCasual User1
74219429.1144554.82568.08000051272016752433163TrueFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalseFalse103.589361HighHighHigh Value Active2
84415191.0718362.31686.882199314122016650103008TrueFalseFalseTrueFalseFalseFalseFalseTrueTrueFalseFalseTrueFalseFalseTrueFalseFalseTrueFalseTrueTrueFalseFalseFalseFalseFalseFalseFalse95.602176LowMediumCasual User2
9451097.3119244.16706.2300004301120161910163022FalseFalseTrueFalseFalseTrueFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseTrueFalseTrueTrueTrueFalseFalseFalseFalseFalseFalseFalse195.749771MediumMediumCasual User2
agedays_since_last_loginavg_time_spentavg_transaction_valuepoints_in_walletchurn_risk_scorejoining_dayjoining_monthjoining_yearlast_visit_time_hourlast_visit_time_minuteslast_visit_time_secondsdays_since_joininggender_Mgender_Unknownregion_category_Townregion_category_Villagemembership_category_Gold Membershipmembership_category_No Membershipmembership_category_Platinum Membershipmembership_category_Premium Membershipmembership_category_Silver Membershipjoined_through_referral_Nojoined_through_referral_Yespreferred_offer_types_Gift Vouchers/Couponspreferred_offer_types_Without Offersmedium_of_operation_Bothmedium_of_operation_Desktopmedium_of_operation_Smartphoneinternet_option_Mobile_Datainternet_option_Wi-Fiused_special_discount_Yesoffer_application_preference_Yespast_complaint_Yesfeedback_Poor Customer Servicefeedback_Poor Product Qualityfeedback_Poor Websitefeedback_Products always in Stockfeedback_Quality Customer Carefeedback_Reasonable Pricefeedback_Too many adsfeedback_User Friendly Websitespend_time_ratiovalue_segmentactivity_segmentjourney_stagecluster
36982451034.93000041558.93703.03000033182016830413113FalseFalseTrueFalseFalseFalseFalseTrueFalseTrueFalseTrueFalseFalseFalseFalseFalseTrueTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse1156.663791HighLowHigh Value Active2
3698345949.33000045358.49242.979625530820161053313114TrueFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueFalseFalseTrueFalseTrueTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalse901.221737HighLowHigh Value Active2
369845124312.33000063446.71778.700000171020161541363076TrueFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalse202.491654HighHighHigh Value Active3
369851213418.38000056397.21725.890000225102016330173058FalseFalseFalseTrueFalseFalseFalseTrueFalseTrueFalseTrueFalseFalseTrueFalseFalseFalseTrueTrueTrueFalseFalseFalseTrueFalseFalseFalseFalse134.477586HighHighHigh Value Active3
369862713135.8300008225.68748.5700003792015529193472TrueFalseTrueFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseTrueFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse60.116056LowMediumCasual User1
36987462-650.68275927277.68639.5100004219201741452727FalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFalse-41.986153MediumLowCasual User0
369882913-638.12342111069.71527.990000527620162318313178FalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseTrueFalseTrueTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalse-17.374514LowLowCasual User0
369892312154.94000038127.56680.47000041192016350253102FalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalseTrueFalseFalseTrueFalseTrueTrueFalseFalseTrueFalseFalseFalseFalseFalse244.501475HighMediumCasual User1
369905315482.6100002378.86197.2644143156201795032825TrueFalseFalseTrueFalseFalseTrueFalseFalseTrueFalseTrueFalseFalseFalseTrueTrueFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse4.918964LowHighEngaged Regular2
36991351579.1800002189.68719.970000223102015139523426TrueFalseTrueFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseTrueFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse27.309554LowLowCasual User1